The Train Automatic Stop Control (TASC) system, which is often part of an Automatic Train Operation (ATO) system, manages the train braking system to stop the train at the predetermined location. The TASC system receives measurements from sensors, on the train and/or on remote stations via communication networks, estimates the state of the train including a position and a velocity of the train, and selects the actions for the braking system. These steps are repeated multiple times until the train stops.
The TASC system allows the trains equipped with TASC to stop automatically at stations without the need to operate the brakes manually. The TASC was originally developed in the 1950s and the 1960s as a way of ensuring that trains stop properly at stations, especially if the driver has made a minor driving lapse and stopped with a slight overrun/underrun. When station platforms are provided with screen doors, the doors of the train must be aligned with the platform doors as otherwise the operation of automatic trains, particularly driverless underground trains, is disrupted.
Most of the conventional methods select the control action in the TASC system according to one of many possible velocity profiles determined based on a distance between the current position of the train and the stop position see, for example, U.S. 2013/0151107. The velocity profile is called a run curve. If a distance along the route is denoted by z, then a desired velocity v(z) at position z describes the run curve. The run curve has to obey legal and mechanical constraints of the route, e.g. speed limits, safety margins, and must be physically realizable by mechanisms of the train.
However, the generation of those velocity profiles are difficult and/or time and resource consuming. In addition, the selection of the optimal velocity profile is prone to errors due to uncertainty of some of the parameters of the movement of the train, such mass of the train mass, and the track friction. In practice, many reference profiles are generated before train operation based on different assumptions of train and environmental parameters, and the one to be used in each operations of stopping is selected based on evaluating the current conditions. However, there is not guarantee that one curve satisfying exactly the current conditions is available, and/or that the current conditions are exactly known, and/or that the conditions do not change during execution of the stopping.
For instance, a run curve can be selected based on high friction of the rails as in the case of dry rails to minimize the stopping time by exploiting the high rail friction. If the rail conditions change during the stopping, for instance due to encountering a section of track where the rails are wet which reduces the rail friction, it may be impossible to achieve the desired braking effort. Hence the train velocity profile would deviate from the run curve, and the train stop pasts its desired stopping point, missing alignment with the station.
Furthermore, separation of trajectory generation and control to follow trajectory can fail to follow the selected run curve exactly due to, e.g., the imprecisions of the braking system, change of the train parameters, and external disturbances, so the train can fail to stop at desired location. In theory, a feedback control can aim to track the selected run curve while reducing the effect of external uncertainties and improperly selected assumptions. However, the feedback control usually cannot provide definite guarantees of the performance in tracking an externally generated signal.
Furthermore, it is in general not optimal to first generate a trajectory and then control the train to follow the trajectory based on feedback from sensors that adjusts to current conditions, due to a two steps design procedure. In addition, the concurrent generation of the trajectory and feedback control action subject to uncertainty in the parameters it is notoriously difficult to achieve because the uncertainty reduces the accuracy of prediction of the future behavior of the train, which is required for optimization.
Accordingly, there is a need to provide a system and a method for stopping a train at a position with an automatic control, but without the predetermined velocity profiles.